Extended pelvic lymph node dissection in patients with prostate cancer previously treated with surgery for lower urinary tract symptoms. Academic Article uri icon

Overview

abstract

  • OBJECTIVES: To evaluate the effect of previous prostate surgery performed for lower urinary tract symptoms (LUTS) on the ability to predict lymph node invasion (LNI) in patients subsequently diagnosed with prostate cancer, testing two widely used LNI predictive models. PATIENTS AND METHODS: From 1990 to 2012, we collected data on 4734 patients with prostate cancer treated with radical prostatectomy and extended pelvic LN dissection (ePLND). Of these, 4453 (94%) had no prior prostate surgery ('naïve patients'), while 286 (6%) had previously undergone surgery for LUTS. Two LNI prediction models based on patients treated with ePLND were evaluated using the receiver operating characteristic-derived area under the curve (AUC), the calibration plot method, and decision-curve analyses. RESULTS: The rate of LNI was 12%, while the median number of LNs removed was 15 in both groups (P = 0.9). The two tested nomograms provided more accurate prediction in naïve patients than for those previously treated with prostate surgery for LUTS (AUC: 82% and 81% vs 68% and 71%, P = 0.01 and P = 0.04, respectively). In naïve patients the surgeon would have missed one LNI for every 53 and 34 avoided ePLND using the Briganti and Godoy nomograms, respectively; in patients previously treated with surgery for LUTS, a LNI would have been missed in 13 and 21 patients not undergoing ePLND. CONCLUSION: The accuracy and the clinical net-benefit of LNI prediction tools decrease significantly in patients with prior prostate surgery for LUTS. These models should be avoided in such patients, who should undergo routine ePLND.

publication date

  • January 21, 2015

Research

keywords

  • Lower Urinary Tract Symptoms
  • Lymph Node Excision
  • Lymph Nodes
  • Prostatic Neoplasms

Identity

Scopus Document Identifier

  • 84938957450

Digital Object Identifier (DOI)

  • 10.1111/bju.12912

PubMed ID

  • 25168453

Additional Document Info

volume

  • 116

issue

  • 3